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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chang, Wei Rorres, Chris Kam, Moshe |
| Copyright Year | 1993 |
| Description | Author affiliation: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA 19104 (Chang, Wei; Kam, Moshe) || Department of Mathematics and Computer Science, Drexel University, Philadelphia, PA 19104 (Rorres, Chris) |
| Abstract | A binary distributed detection system comprises a bank of local decision makers (LDMs) and a central information processor (the data fusion center, DFC). All LDMs survey a common volume for a binary {H0,H1} phenomenon. Each LDM forms a binary decision: it either accepts H1 ("target-present") or H0 ("target-absent"). The LDM is fully characterized by its performance probabilities (probability of false alarm and probaility of detection). The decisions are transmitted to the DFC through noiseless communication channels. The DFC then optimally combines the local decisions to obtain a global decision ("target-present" or "target-absent") which minimizes a Bayesian objective function. The main difference between the present study and previous ones is that, along with the local decisions, the DFC in our architecutre remembers and uses its most recent decision in synthesizing each new decision. We show that this feature endows our architecture with a detection performance that is generally much better than that of a memoryless DFC system. Moreover, when operating in a stationary environment, our architecture converges to a steady-state decision in finite time with probability one, and its detection performance during convergence and in steady state is strictly determined. |
| Starting Page | 161 |
| Ending Page | 165 |
| File Size | 475936 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780308603 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-06-02 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | American Automatic Control Council(AACC) |
| Subject Keyword | Digital-to-frequency converters Communication channels Steady-state Performance analysis Ear Distributed computing Mathematics Computer science Bayesian methods Convergence |
| Content Type | Text |
| Resource Type | Article |
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